Neural ADMIXTURE is an unsupervised global ancestry inference technique based on ADMIXTURE. By using neural networks, Neural ADMIXTURE offers high quality ancestry assignments with a running time which is much faster than ADMIXTURE's. For more information, we recommend reading our corresponding article.
The software can be invoked via CLI and has a similar interface to ADMIXTURE (e.g. the output format is completely interchangeable). While the software runs in both CPU and GPU, we recommend using GPUs if available to take advantage of the neural network-based implementation.
The successful usage of this package requires a computer with enough RAM to be able to handle the large datasets the network has been designed to work with. Due to this, we recommend using compute clusters whenever available to avoid memory issues.
The package has been tested on both Linux (CentOS 7.9.2009, Ubuntu 18.04.5 LTS) and MacOS (BigSur 11.2.3, Intel and Monterey 12.3.1, M1). It is highly recommended to use GPUs for optimal performance - make sure CUDA drivers are properly installed.
We recommend creating a fresh Python 3.12 environment using conda (or virtualenv), and then install the package neural-admixture there. As an example, for conda, one should launch the following commands:
$ conda create -n nadmenv python=3.12
$ conda activate nadmenv
(nadmenv) $ pip install neural-admixtureImportant note: Using GPUs greatly speeds up processing and is recommended for large datasets.
Specify the number of GPUs (--num_gpus) and CPUs (--num_cpus) you have available in your machine to optimize the performance. For MacOS users with an Apple Metal chip, using --num_gpus 1 will enable MPS acceleration in the software. Note that despite MPS acceleration being supported, the RAM available in laptops is probably limited, so larger datasets should be run on CUDA-capable GPUs, which the software is more optimized for.
To train a model from scratch, simply invoke the following commands from the root directory of the project. For more info about all the arguments, please run neural-admixture train --help. If training a single-head version of the network suffices, please use the flag --k instead of --min_k and --max_k. Note that BED, PGEN and VCF are supported as of now.
For unsupervised Neural ADMIXTURE (single-head):
$ neural-admixture train --k K --name RUN_NAME --data_path DATA_PATH --save_dir SAVE_PATHFor unsupervised Neural ADMIXTURE (multi-head):
$ neural-admixture train --min_k K_MIN --max_k K_MAX --name RUN_NAME --data_path DATA_PATH --save_dir SAVE_PATHFor supervised Neural ADMIXTURE:
$ neural-admixture train --k K --supervised --populations_path POPS_PATH --name RUN_NAME --data_path DATA_PATH --save_dir SAVE_PATH # only single-head support at the momentAs an example, the following ADMIXTURE call
$ ./admixture snps_data.bed 8 -s 42would be mimicked in Neural ADMIXTURE by running
$ neural-admixture train --k 8 --data_path snps_data.bed --save_dir SAVE_PATH --init_file INIT_FILE --name snps_data --seed 42with some parameters such as the decoder initialization or the save directories not having a direct equivalent.
Several files will be output to the SAVE_PATH directory (the name parameter will be used to create the whole filenames):
- A
.Pfile, similar to ADMIXTURE. - A
.Qfile, similar to ADMIXTURE. - A
.ptfile, containing the weights of the trained network. - A
.jsonfile, with the configuration of the network.
The last three files are required to run posterior inference using the network, so be aware of not deleting them accidentally! Logs are printed to the stdout channel by default. If you want to save them to a file, you can use the command tee along with a pipe:
$ neural-admixture train --k 8 ... | tee run.logADMIXTURE allows reusing computations in the projective analysis mode, in which the P (F, frequencies) matrix is fixed to an already known result and only the assignments are computed. Due to the nature of our algorithm, assignments can be computed for unseen data by simply feeding the data through the encoder. This mode can be run by typing infer instead of train right after the neural-admixture call.
For example, assuming we have a trained Neural ADMIXTURE (named nadm_test) in the path ./outputs, one could run inference on unseen data (./data/unseen_data.bed) via the following command:
$ neural-admixture infer --name nadm_test --save_dir ./outputs --out_name unseen_nadm_test --data_path ./data/unseen_data.bedFor this command to work, files ./outputs/nadm_test.pt and ./outputs/nadm_test_config.json, which are training outputs, must exist. In this case, only a .Q will be created, which will contain the assignments for this data (the parameter of the flag out_name will be used to generate the output file name). This file will be written in the --save_dir directory (in this case, ./outputs).
The supervised version of the algorithm can be used when all samples have a corresponding population label. This can be very benificial, especially when dealing with large imbalances in the data (e.g data contains 1K samples from Pop1 and 50 samples from Pop2).
In order to use the supervised mode, the --pops_path argument pointing to the file where the ancestries are defined must be passed. The latter file must be a single-column, headerless, plain text file where row i denotes the ancestry for the i-th sample in the data. We currently do not support datasets which contain samples with missing ancestries.
The supervised mode works by adding a scaled classification loss to the bottleneck of the algorithm (Equation 5 of the paper). The scaling factor can have a big impact on the performance. If it is too small, then the supervised loss will have little impact on the training, so results would be similar to an unsupervised run. On the other hand, if it is too large, then the supervision will dominate training, making the network overconfident in its predictions: essentially, one would get only binary assignments. The default value of the scaling factor is _η=100, and can be controlled using the parameter --supervised_loss_weight.
Basically, if on validation data you are getting single-ancestry estimations when you expect admixed estimations, try setting a smaller value for the supervised loss scaling factor η (--supervised_loss_weight).
Moreover, note that the initialization method chosen will have no effect, as the supervised method is always used when using the supervised version.
batch_size: number of samples used at every update. If you have memory issues, try setting a lower batch size. Defaults to 800.n_components: dimension of the PCA projection for SVD. Defaults to 8.epochs: maximum number of times the whole training dataset is used to update the weights. Defaults to 250.learning_rate: dictates how large an update to the weights will be. If you find the loss function oscillating, try setting a lower value. If convergence is slow, try setting a higher value. Defaults to 25e-4.seed: RNG seed for replication purposes. Defaults to 42.num_gpus: number of GPUs to use during training. Set to 0 for CPU-only execution. Defaults to 0.
The datasets All-Chms, Chm-22 and Chm-22-Sim used in the Experiments section of the article can be found in figshare. For descriptions of the datasets, please refer to the corresponding section in the paper. The exact hyperparameters used in the experiments to allow replication can be found in the Supplementary Table 3 of the article.
To run the software with a small demo dataset, check the instructions in the corresponding folder of the repository.
If you get an error similar to the following (when using GPU):
OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
simply installing nvcc using conda/mamba should fix it:
$ conda install -c nvidia nvccNOTICE: This software is available for use free of charge for academic research use only. Academic users may fork this repository and modify and improve to suit their research needs, but also inherit these terms and must include a licensing notice to that effect. Commercial users, for profit companies or consultants, and non-profit institutions not qualifying as "academic research" should contact the authors for a separate license. This applies to this repository directly and any other repository that includes source, executables, or git commands that pull/clone this repository as part of its function. Such repositories, whether ours or others, must include this notice.
When using this software, please cite the following paper:
@article{dominguezmantes23,
abstract = {Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by computing multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.},
author = {Dominguez Mantes, Albert and Mas Montserrat, Daniel and Bustamante, Carlos D. and Gir{\'o}-i-Nieto, Xavier and Ioannidis, Alexander G.},
doi = {10.1038/s43588-023-00482-7},
id = {Dominguez Mantes2023},
isbn = {2662-8457},
journal = {Nature Computational Science},
title = {Neural ADMIXTURE for rapid genomic clustering},
url = {https://doi.org/10.1038/s43588-023-00482-7},
year = {2023},
bdsk-url-1 = {https://doi.org/10.1038/s43588-023-00482-7}}